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作者： Azad, Chandrashekhar Bhushan, Bharat Sharma, Rohit Shankar, Achyut Singh, Krishna Kant Khamparia, Aditya Department of Computer Applications National Institute of Technology Jamshedpur India Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida India Department of Electronics & Communication Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology NCR Campus Delhi- NCR Campus Ghaziabad India Department of CSE ASET Amity University Noida201301 India Bengaluru India School of Computer Science and Engineering Lovely Professional University Punjab India

Diabetes mellitus is a well-known chronic disease that diminishes the insulin producing capability of the human body. This results in high blood sugar level which might lead to various complications such as eye damage, nerve damage, cardiovascular damage, kidney damage and stroke. Although diabetes has attracted huge research attention, the overall performance of such medical disease classification using machine learning techniques is relatively low, majorly due to existence of class imbalance and missing values in the data. In this paper, we propose a novel Prediction Model using Synthetic Minority Oversampling Technique, Genetic Algorithm and Decision Tree (PMSGD) for Classification of Diabetes Mellitus on Pima Indians Diabetes Database (PIDD) dataset. The framework of the proposed PMSGD prediction model is composed of four different layers. The first layer is the pre-processing layer which is responsible for handling missing values, detection of outlier and oversampling the minority class. In the second layer, the most significant features are selected using correlation and genetic algorithm. In the third layer, the proposed model is trained, and its effectiveness is evaluated in the fourth layer in terms of classification accuracy (CA), classification error (CE), precision, recall (sensitivity), measure (FM), and Area_Under_ROC (AUROC). The proposed PMSGD algorithm clearly outperforms its counterparts and achieves a remarkable accuracy of 82.1256%. The best outcome achieved by the proposed system in terms of CA, CE, precision, sensitivity, FM and AUROC is 82.1256%, 17.8744%, 0.8070%, 0.8598, 0.8326 and 0.8511, respectively. The obtained simulation results show the effectiveness and superiority of our proposed PMSGD model and their by reduced error rate to help in decision-making process. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Decision trees

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Achieving concurrency in cloud-orchestrated Internet of Things for resource sharing through multiple concurrent access

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Computational Intelligence 2024年第1期40卷 e12630-e12630页

作者： Xu, Xuedong Sun, Wei Vivekananda, G.N. Shankar, Achyut Department of Mechanical Engineering School of Mechanical and Electrical Engineering Changchun Institute of Technology Changchun China School of Architecture and Design Changchun Institute of Technology Changchun China Department of CSE Madanapalle Institute of Technology & Science India Department of computer science and Engineering ASET Amity University Noida India

Cloud-orchestrated Internet of Things (IoT) facilitates proper utilization of network resources and placating user demands in smart communications. Multiple concurrent access (MCA) techniques designed for cloud-assisted communication helps to achieve better resource sharing features with fault tolerance ability. A multi-objective resource allocation and sharing (RAS) for balancing MCA in cloud-orchestrated IoT is presented in this article. The RAS constraints are modeled through linear programming (LP) as an optimization approach. The constraints are resolved using genetic representations (GR) for reducing the unserviced requests and failed resource allocations. Conventional genetic stages are inherited by the LP model to solve resource allocation and access issues reducing latency. The combined LP and GR jointly resolve resource allocation and MCA stagnation in cloud network. A fair outcome of LP-GR is estimation using the metrics response latency, resource utilization, request handled, and average latency. © 2020 Wiley Periodicals LLC.

关键词： Linear programming

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Disease Detection and Quality Analysis of Fruits and Vegetables 2nd

Disease Detection and Quality Analysis of Fruits and Vegetab...

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2nd International Conference on Future Learning Aspects of Mechanical engineering, FLAME 2020

作者： Tyagi, Shweta Uppal, Anmol Kumar, Rishi Sharma, Seema Department of Computer Science and Engineering ASET Amity University Noida India Department of CIS Universiti Teknologi Petronas Seri Iskandar Malaysia

ISBN: (纸本)9789811599552

The growing requirement for object recognition techniques which are more "robust and efficient", is in great demand and highly researched field. Checking the quality of any fruit or vegetable is tedious task and time consuming. The diseases present in the fruits and vegetables decreases the quality and the productivity. There is more involvement of scientists, mall owners and labor to identify the defected part in vegetables and fruits. This whole process consumes a lot of time which in turn damage the rest of production and result cataclysmic for farmers. To reduce the problem, we propose a method for the same as Disease Detection and Quality Analysis of Fruits and Vegetables. Implementing this proposed method can automate quality analysis and disease detection, furthermore consuming less time which in turn can be very beneficial and make it work more efficiently. It has tremendous real-life deployments which includes usage with wide of range processes and systems. This method can be used by the industrial systems or even in the smart home systems, which has even a wider scope. The industries which can use this system are cold storage market and farming industry to analyze fruits and vegetable disease. In addition, the method is entirely powerful against other systems because of the great level of automation of disease detection, which helps the medical research domain. Execution of this application will be a new addition automated and smart procedures. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Automate quality analysis Disease detection Food and vegetable diseases Medical research Quality of fruits and vegetables

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Correction to: A modified social spider algorithm for an efficient data dissemination in VANET

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Environment, Development and Sustainability 2022年 1-2页

作者： Shankar, Achyut Dayalan, Rajaguru Chakraborty, Chinmay Dhasarathan, Chandramohan Kumar, Manish Department of Computer Science and Engineering ASET Amity University Noida India Department of Computer Science & Engineering Pondicherry University Puducherry India Department of Electronics & Communication Engineering Birla Institute of Technology Ranchi India Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Patiala India

In the original publication of the article [1], affiliation of the authors Chandramohan

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Comparison of Affiliate Programs Strategies Using Different Platforms

Comparison of Affiliate Programs Strategies Using Different ...

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Advanced Computing & Communication Technologies (ICACCTech), International Conference on

作者： Ravi Kumar Kapil Kumar Ankit Tomar Paras Jain Lipika Goel Vishan Kumar Gupta Computer Science & Engineering COER University Roorkee India Computer Science & Engineering Graphic Era Deemed to be University Dehradun India SCEAI VIT Bhopal University Kothri Kalan Bhopal MP India Department of AIML Sridevi Women's Engineering College Hyderabad India ASET Amity University Punjab Mohali Punjab India

ISBN: (数字)9798331519056

ISBN: (纸本)9798331519063

This research about the specific objectives of given affiliate programs will involve comparison of the significant aspects that include commission plans, program specifics, marketing tools, payment options, as well as affiliate assistance. This abstract provides an overview of a comparative analysis of affiliate program strategies employed by five prominent platforms:, some of the most popular affiliating programs that are available include the amazon associates, eBay partner network, Walmart associates, Rakuten affiliate, CJ affiliate and ShareASale. Primary and secondary data sources are used to gather information on the official website, and terms and conditions of programs and industry reports. Since a number of values would be numeric, for example, commission rate, cookie duration, and payout threshold, statistical methods will be employed; program features and other promotional tools will be assessed through content analysis. Besides, this research will help fill the gap in the literature on affiliate marketing strategies, examining the development and directions of this type of activity.

关键词： Industries Content management Statistical analysis Soft sensors Roads Focusing Communications technology Advertising Business

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Prediction of heart disease using machine learning algorithms

Prediction of heart disease using machine learning algorithm...

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2020 International Conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2020

作者： Choudhary, Garima Narayan Singh, Shailendra ASET Amity University Computer Science Engineering Noida India

ISBN: (纸本)9781728172132

For medical purposes, the diagnosis of heart sickness is one of the difficult ventures. A known range of available datasets leads to numerous options with the help of analyzing the data and visual analytics. In this proposed work we have analyzed the features for which we can implement a great system from a large amount of available data. In the absence of useful information, massive statistics can lead to vague outcomes. It relies upon careful evaluation of various medical and pathological facts of the sufferers obtained through health workers, in a complex manner. For validating the performance of proposed work, it applied on complete traits and on decreased bunch of traits. Classifier performance is controlled by feature reduction with respect to precision and implementation time of classifier. Doctors can be assisted in diagnosing heart patients effectively through machine learning. So, study has been made of heart sickness for all age group sufferers by assistance of machine learning algorithms. Decision tree algorithm has been used to make the predictions whether a person has heart sickness or not followed by the Ada-Boost algorithm. Among the applied classifier importance of features have been identified. © 2020 IEEE.

关键词： Decision trees

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Behavioral Analytics for Predictive Modeling of Mental Health Disorders: A Review of Machine Learning Techniques and Challenges

Behavioral Analytics for Predictive Modeling of Mental Healt...

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Intelligent Control, Computing and Communications (IC3), International Conference on

作者： Prashant Vats Pravin R. Kshirsagar Kamal Upreti Keshav Lalit Tan Kuan Tak Shubham Mahajan Dept. of Computer Science and Engg Manipal University Jaipur Jaipur India Department of Electronics & Telecommunication Engineering J D College of Engineering & Management Nagpur India Dept. of Computer Science CHRIST (Deemed to Be University) Delhi NCR India Dept. of Computer Science and Engg Manipal Institute of Technology Manipal Karnataka India Engineering Cluster Singapore Institute of Technology Singapore Amity School of Engineering and Technology (ASET) Amity University Gurugram India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

Mental health issues, including anxiety, stress, and depression, may remain untreated until they escalate to a severe level. The issues significantly impact an individual's overall well-being and productivity. Timely identification is crucial for the effectiveness of both intervention and therapy. The application of machine learning techniques makes behavioral analytics a powerful tool for mental health disease prediction modeling. By analyzing behavioral data, this technology facilitates the early detection of various illnesses. This work aims to provide a thorough overview of the use of machine learning techniques, including models that employ Deep structured learning as well as both unsupervised as well as supervised learning, to behavioral data, including activity levels, speech patterns, and facial movements, in order to identify signs of mental health. The benefits and drawbacks of a broad range of machine learning algorithms are examined, with a focus on how these computer algorithms may be applied to identify patterns linked to illnesses like stress, anxiety, and emotional depression. This study looks into the problems that this business encounters as well. These difficulties include combining behavioral data with extra environmental issues and physiological features from the immediate surroundings, the necessity for large and diverse datasets, the need for security of information, and the capacity to understand models.

关键词： Deep learning Analytical models Machine learning algorithms Computational modeling Anxiety disorders Mental health Predictive models Depression Physiology Long short term memory

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Comparative analysis of machine learning techniques for brain tumor images 8

Comparative analysis of machine learning techniques for brai...

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8th International Conference on Signal Processing and Integrated Networks, SPIN 2021

作者： Singh, Ratima Raj Kumar, Sumit Vijh, Surbhi Department of Computer Science and Engineering ASET Amity University Uttar Pradesh Noida India Department of Information Technology KIET Group of Institution Ghaziabad India

ISBN: (纸本)9781665435642

Medical advancement maintaining medical information by removing several irrelevant and unwanted features. Meta-heuristics are optimization strategies that offer an excellent approach by exploring and exploiting the whole search space to solve this problem and prove that the selection of the feature can be an efficient solution. However, the enormous data size increases dimensionality and reduces classification accuracy. A comparative analysis of metaheuristic algorithms is performed to diagnose the tumor in brain magnetic resonance images (MRI). The observation shows that particle Swarm Optimization (PSO) is giving the highest accuracy of 96.42% with Random Forest (RF) classifier and also obtaining the sensitivity, specificity, precision, negative predictive value (NPV), and F-score of 92.23%,100%, 100%,93.75% and 96.77 respectively. © 2021 IEEE

关键词： Tumors

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An Efficient Brain Tumor Detection Using Modified Tree Growth Algorithm and Random Forest Method 6

An Efficient Brain Tumor Detection Using Modified Tree Growt...

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6th International Conference on Image Information Processing, ICIIP 2021

作者： Singh, Ratima Raj Vijh, Surbhi Chaubey, Diwakar Amity University Uttar Pradesh Department of Computer Science and Engineering Aset Noida India Kiet Group of Institutions Department of Information Technology Uttar Pradesh India

ISBN: (纸本)9781665433617

Medical image processing techniques are believed to diagnose the tumor to improve the patient's survival rate. This paper proposed an improved nature-based Tree Growth algorithm (TGA) for selecting the finest features from the feature set derived by the Local Binary Pattern (LBP) and Gray level co-occurrence matrix (GLCM), the images from Brain Magnetic resonance images (MRI) classified as tumor or non-Tumor by Random Forest (RF) classifier. The significance of this paper is the creation of an intelligent brain tumor diagnostic system using a Chaotic modified binary tree growth algorithm (CMBTGA-RF). The chaotic map, crossover, and mutation operators are implemented in an updated binary tree growth algorithm for improving exploitation and exploration behaviours. The performance of the proposed methodology is assessed with accuracy, sensitivity, specificity, precision, negative predictive value (NPV), and F-score of 96.42%, 100%, 94.11%, 91.66%, 100%, and 96.96% respectively. The algorithmic findings demonstrate that the CMBTGA-RF with Logistic map works better than the nature-based traditional and recent meta-heuristic algorithms. © 2021 IEEE.

关键词： Tumors

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A Hybrid Deep Learning Framework for IoT Security Enhancement and Anomaly Detection

A Hybrid Deep Learning Framework for IoT Security Enhancemen...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： D Baswaraj Abdur Rahman Deependra Pandey T Bhargavi Muslimbek Ismoilov Natha Deepthi Department of Computer Science and Engineering Vasavi College of Engineering Hyderabad India School of Business & Technology Emporia State University Emporia Kansas USA Department of Electronics and Communication Engineering Amity School of Engineering & Technology (ASET) Amity University Lucknow India Department of Information Technology St. Martins Engineering College Hyderabad India Department of Electrical Engineering Fergana Polytechnic Institute Fergana Uzbekistan Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Cyber threats targeting Internet-of-Things(IoT) devices have grown in frequency and severity alongside the IoT's fast uptake and usage in the past few years. Because of this, creating a model that can detect hostile attacks and lessen security risks in IoT-devices is becoming a very important subject. Botnets pose a significant risk because they disrupt the networks that Internet of Things (IoT) devices rely on to function. An method called GA-HDLAD, which stands for genetic-algorithm with hybrid deep learning(DL), was created in this study to advance security in the IoT environment by detecting botnets. Botnet detection makes use of hybrid deep learning, which combines attention concepts, feature-extraction-techniques(FETs), and recurrent-neural-networks(RNNs). The hybrid-DL approach can identify botnet attacks, which frequently use complicated patterns. In addition, the model's RNNs capture temporal dependencies, while FETs guarantee efficient feature extraction from geographical data. To choose the hyperparameters required by the HDL method, simulated annealing (SA) is employed. This paper compares the GA-HDLAD system to other detection approaches and finds that it outperforms them experimentally using a benchmark botnet dataset.

关键词： Deep learning Integrated circuits Botnet Field effect transistors Simulated annealing Feature extraction Internet of Things Security Hardware design languages Anomaly detection

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